{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Neural Networks"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Neural network is a network or circuit of biological neurons and composed of artificial neurons or nodes. It is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that is similar to the way the human brain operates."
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:08.119Z",
          "iopub.execute_input": "2022-11-13T03:39:08.123Z",
          "iopub.status.idle": "2022-11-13T03:39:10.412Z",
          "shell.execute_reply": "2022-11-13T03:39:10.404Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "symbol = 'MSFT'\n",
        "start = '2018-01-01'\n",
        "end = '2022-11-10'\n",
        "\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "                                Open       High        Low      Close  \\\nDate                                                                    \n2018-01-02 00:00:00-05:00  86.129997  86.309998  85.500000  85.949997   \n2018-01-03 00:00:00-05:00  86.059998  86.510002  85.970001  86.349998   \n2018-01-04 00:00:00-05:00  86.589996  87.660004  86.570000  87.110001   \n2018-01-05 00:00:00-05:00  87.660004  88.410004  87.430000  88.190002   \n2018-01-08 00:00:00-05:00  88.199997  88.580002  87.599998  88.279999   \n\n                           Adj Close    Volume  \nDate                                            \n2018-01-02 00:00:00-05:00  81.168518  22483800  \n2018-01-03 00:00:00-05:00  81.546249  26061400  \n2018-01-04 00:00:00-05:00  82.263977  21912000  \n2018-01-05 00:00:00-05:00  83.283890  23407100  \n2018-01-08 00:00:00-05:00  83.368866  22113000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-01-02 00:00:00-05:00</th>\n      <td>86.129997</td>\n      <td>86.309998</td>\n      <td>85.500000</td>\n      <td>85.949997</td>\n      <td>81.168518</td>\n      <td>22483800</td>\n    </tr>\n    <tr>\n      <th>2018-01-03 00:00:00-05:00</th>\n      <td>86.059998</td>\n      <td>86.510002</td>\n      <td>85.970001</td>\n      <td>86.349998</td>\n      <td>81.546249</td>\n      <td>26061400</td>\n    </tr>\n    <tr>\n      <th>2018-01-04 00:00:00-05:00</th>\n      <td>86.589996</td>\n      <td>87.660004</td>\n      <td>86.570000</td>\n      <td>87.110001</td>\n      <td>82.263977</td>\n      <td>21912000</td>\n    </tr>\n    <tr>\n      <th>2018-01-05 00:00:00-05:00</th>\n      <td>87.660004</td>\n      <td>88.410004</td>\n      <td>87.430000</td>\n      <td>88.190002</td>\n      <td>83.283890</td>\n      <td>23407100</td>\n    </tr>\n    <tr>\n      <th>2018-01-08 00:00:00-05:00</th>\n      <td>88.199997</td>\n      <td>88.580002</td>\n      <td>87.599998</td>\n      <td>88.279999</td>\n      <td>83.368866</td>\n      <td>22113000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:10.420Z",
          "iopub.execute_input": "2022-11-13T03:39:10.424Z",
          "iopub.status.idle": "2022-11-13T03:39:12.426Z",
          "shell.execute_reply": "2022-11-13T03:39:12.511Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "                                 Open        High         Low       Close  \\\nDate                                                                        \n2022-11-03 00:00:00-04:00  220.089996  220.410004  213.979996  214.250000   \n2022-11-04 00:00:00-04:00  217.550003  221.589996  213.429993  221.389999   \n2022-11-07 00:00:00-05:00  221.990005  228.410004  221.279999  227.869995   \n2022-11-08 00:00:00-05:00  228.699997  231.649994  225.839996  228.869995   \n2022-11-09 00:00:00-05:00  227.369995  228.630005  224.330002  224.509995   \n\n                            Adj Close    Volume  \nDate                                             \n2022-11-03 00:00:00-04:00  214.250000  36633900  \n2022-11-04 00:00:00-04:00  221.389999  36767800  \n2022-11-07 00:00:00-05:00  227.869995  33498000  \n2022-11-08 00:00:00-05:00  228.869995  28192500  \n2022-11-09 00:00:00-05:00  224.509995  27852900  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2022-11-03 00:00:00-04:00</th>\n      <td>220.089996</td>\n      <td>220.410004</td>\n      <td>213.979996</td>\n      <td>214.250000</td>\n      <td>214.250000</td>\n      <td>36633900</td>\n    </tr>\n    <tr>\n      <th>2022-11-04 00:00:00-04:00</th>\n      <td>217.550003</td>\n      <td>221.589996</td>\n      <td>213.429993</td>\n      <td>221.389999</td>\n      <td>221.389999</td>\n      <td>36767800</td>\n    </tr>\n    <tr>\n      <th>2022-11-07 00:00:00-05:00</th>\n      <td>221.990005</td>\n      <td>228.410004</td>\n      <td>221.279999</td>\n      <td>227.869995</td>\n      <td>227.869995</td>\n      <td>33498000</td>\n    </tr>\n    <tr>\n      <th>2022-11-08 00:00:00-05:00</th>\n      <td>228.699997</td>\n      <td>231.649994</td>\n      <td>225.839996</td>\n      <td>228.869995</td>\n      <td>228.869995</td>\n      <td>28192500</td>\n    </tr>\n    <tr>\n      <th>2022-11-09 00:00:00-05:00</th>\n      <td>227.369995</td>\n      <td>228.630005</td>\n      <td>224.330002</td>\n      <td>224.509995</td>\n      <td>224.509995</td>\n      <td>27852900</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.433Z",
          "iopub.execute_input": "2022-11-13T03:39:12.438Z",
          "iopub.status.idle": "2022-11-13T03:39:12.449Z",
          "shell.execute_reply": "2022-11-13T03:39:12.518Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ret = 100 * (df.pct_change()[1:]['Adj Close'])\n",
        "realized_vol = ret.rolling(5).std()"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.457Z",
          "iopub.execute_input": "2022-11-13T03:39:12.462Z",
          "iopub.status.idle": "2022-11-13T03:39:12.471Z",
          "shell.execute_reply": "2022-11-13T03:39:12.535Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(realized_vol.index,realized_vol)\n",
        "plt.title('Realized Volatility - ' + symbol)\n",
        "plt.ylabel('Volatility')\n",
        "plt.xlabel('Date')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.479Z",
          "iopub.execute_input": "2022-11-13T03:39:12.486Z",
          "shell.execute_reply": "2022-11-13T03:39:12.719Z",
          "iopub.status.idle": "2022-11-13T03:39:12.621Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "realized_vol = ret.rolling(5).std()\n",
        "realized_vol = pd.DataFrame(realized_vol)\n",
        "realized_vol.reset_index(drop=True, inplace=True)"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.627Z",
          "iopub.execute_input": "2022-11-13T03:39:12.633Z",
          "iopub.status.idle": "2022-11-13T03:39:12.640Z",
          "shell.execute_reply": "2022-11-13T03:39:12.722Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "returns_svm = ret ** 2\n",
        "returns_svm = returns_svm.reset_index()\n",
        "del returns_svm['Date']"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.648Z",
          "iopub.execute_input": "2022-11-13T03:39:12.652Z",
          "shell.execute_reply": "2022-11-13T03:39:12.725Z",
          "iopub.status.idle": "2022-11-13T03:39:12.659Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        " X = pd.concat([realized_vol, returns_svm], axis=1, ignore_index=True)\n",
        " X = X[4:].copy()\n",
        " X = X.reset_index()\n",
        " X.drop('index', axis=1, inplace=True)"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.666Z",
          "iopub.execute_input": "2022-11-13T03:39:12.671Z",
          "iopub.status.idle": "2022-11-13T03:39:12.678Z",
          "shell.execute_reply": "2022-11-13T03:39:12.728Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "realized_vol = realized_vol.dropna().reset_index()\n",
        "realized_vol.drop('index', axis=1, inplace=True)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:12.685Z",
          "iopub.execute_input": "2022-11-13T03:39:12.689Z",
          "iopub.status.idle": "2022-11-13T03:39:12.695Z",
          "shell.execute_reply": "2022-11-13T03:39:12.731Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n = 252\n",
        "NN_vol = MLPRegressor(learning_rate_init=0.001, random_state=1)\n",
        "\n",
        "para_grid_NN = {'hidden_layer_sizes': [(100, 50), (50, 50), (10, 100)],\n",
        " 'max_iter': [500, 1000],\n",
        " 'alpha': [0.00005, 0.0005 ]}\n",
        "\n",
        "clf = RandomizedSearchCV(NN_vol, para_grid_NN)\n",
        "clf.fit(X.iloc[:-n].values, realized_vol.iloc[1:-(n-1)].values.reshape(-1, ))\n",
        "\n",
        "predictions = clf.predict(X.iloc[-n:])"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-11-13T03:39:28.815Z",
          "iopub.status.busy": "2022-11-13T03:39:12.702Z",
          "iopub.execute_input": "2022-11-13T03:39:12.706Z",
          "iopub.status.idle": "2022-11-13T03:39:28.674Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = pd.DataFrame(predictions)\n",
        "predictions.index = ret.iloc[-n:].index"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-11-13T03:39:28.819Z",
          "iopub.status.busy": "2022-11-13T03:39:28.685Z",
          "iopub.execute_input": "2022-11-13T03:39:28.693Z",
          "iopub.status.idle": "2022-11-13T03:39:28.703Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "                                  0\nDate                               \n2021-11-10 00:00:00-05:00  1.015868\n2021-11-11 00:00:00-05:00  0.836410\n2021-11-12 00:00:00-05:00  1.117513\n2021-11-15 00:00:00-05:00  1.030438\n2021-11-16 00:00:00-05:00  1.136596\n...                             ...\n2022-11-03 00:00:00-04:00  2.754927\n2022-11-04 00:00:00-04:00  2.713711\n2022-11-07 00:00:00-05:00  3.004522\n2022-11-08 00:00:00-05:00  2.827585\n2022-11-09 00:00:00-05:00  2.559905\n\n[252 rows x 1 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-11-10 00:00:00-05:00</th>\n      <td>1.015868</td>\n    </tr>\n    <tr>\n      <th>2021-11-11 00:00:00-05:00</th>\n      <td>0.836410</td>\n    </tr>\n    <tr>\n      <th>2021-11-12 00:00:00-05:00</th>\n      <td>1.117513</td>\n    </tr>\n    <tr>\n      <th>2021-11-15 00:00:00-05:00</th>\n      <td>1.030438</td>\n    </tr>\n    <tr>\n      <th>2021-11-16 00:00:00-05:00</th>\n      <td>1.136596</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-11-03 00:00:00-04:00</th>\n      <td>2.754927</td>\n    </tr>\n    <tr>\n      <th>2022-11-04 00:00:00-04:00</th>\n      <td>2.713711</td>\n    </tr>\n    <tr>\n      <th>2022-11-07 00:00:00-05:00</th>\n      <td>3.004522</td>\n    </tr>\n    <tr>\n      <th>2022-11-08 00:00:00-05:00</th>\n      <td>2.827585</td>\n    </tr>\n    <tr>\n      <th>2022-11-09 00:00:00-05:00</th>\n      <td>2.559905</td>\n    </tr>\n  </tbody>\n</table>\n<p>252 rows × 1 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:28.711Z",
          "iopub.execute_input": "2022-11-13T03:39:28.718Z",
          "iopub.status.idle": "2022-11-13T03:39:28.730Z",
          "shell.execute_reply": "2022-11-13T03:39:28.822Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "realized_vol "
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "      Adj Close\n0      0.541096\n1      0.698926\n2      0.631679\n3      0.832579\n4      1.142363\n...         ...\n1214   2.966618\n1215   2.670685\n1216   3.225606\n1217   3.138010\n1218   2.722452\n\n[1219 rows x 1 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Adj Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.541096</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.698926</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.631679</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.832579</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.142363</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1214</th>\n      <td>2.966618</td>\n    </tr>\n    <tr>\n      <th>1215</th>\n      <td>2.670685</td>\n    </tr>\n    <tr>\n      <th>1216</th>\n      <td>3.225606</td>\n    </tr>\n    <tr>\n      <th>1217</th>\n      <td>3.138010</td>\n    </tr>\n    <tr>\n      <th>1218</th>\n      <td>2.722452</td>\n    </tr>\n  </tbody>\n</table>\n<p>1219 rows × 1 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:28.740Z",
          "iopub.execute_input": "2022-11-13T03:39:28.748Z",
          "iopub.status.idle": "2022-11-13T03:39:28.761Z",
          "shell.execute_reply": "2022-11-13T03:39:28.825Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = np.sqrt(mse(realized_vol.iloc[-n:] / 100, predictions / 100))\n",
        "\n",
        "print('The RMSE value of Neural Network is {:.6f}'.format(rmse))\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The RMSE value of Neural Network is 0.001746\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:28.770Z",
          "iopub.execute_input": "2022-11-13T03:39:28.775Z",
          "iopub.status.idle": "2022-11-13T03:39:28.787Z",
          "shell.execute_reply": "2022-11-13T03:39:28.828Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df.index[:1219], realized_vol / 100, label='Realized Volatility')\n",
        "plt.plot(predictions / 100, label='Volatility Prediction in NN')\n",
        "plt.title(symbol + ' Volatility Prediction with Neural Network', fontsize=12)\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-13T03:39:28.794Z",
          "iopub.execute_input": "2022-11-13T03:39:28.799Z",
          "iopub.status.idle": "2022-11-13T03:39:28.857Z",
          "shell.execute_reply": "2022-11-13T03:39:28.862Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}